Concurrent validity of computer-vision artificial intelligence player tracking software using broadcast footage
Zachary L. Crang, Rich D. Johnston, Katie L. Mills, Johsan Billingham, Sam Robertson, Michael H. Cole, Jonathon Weakley, Adam Hewitt and, Grant M. Duthie

TL;DR
This study evaluates the accuracy of commercial computer-vision AI player tracking software using broadcast footage from a FIFA match, highlighting the impact of camera feed and resolution on measurement precision.
Contribution
It provides empirical validation of AI tracking software against a high-definition multi-camera system, assessing accuracy and influencing factors in a real-world sports setting.
Findings
Position RMSE ranged from 1.68 to 16.39 meters
Speed RMSE ranged from 0.34 to 2.38 m/s
Match distance bias varied from -1745 to 1945 meters
Abstract
This study aimed to: (1) understand whether commercially available computer-vision and artificial intelligence (AI) player tracking software can accurately measure player position, speed and distance using broadcast footage and (2) determine the impact of camera feed and resolution on accuracy. Data were obtained from one match at the 2022 Qatar Federation Internationale de Football Association (FIFA) World Cup. Tactical, programme and camera 1 feeds were used. Three commercial tracking providers that use computer-vision and AI participated. Providers analysed instantaneous position (x, y coordinates) and speed (m\,s^{-1}) of each player. Their data were compared with a high-definition multi-camera tracking system (TRACAB Gen 5). Root mean square error (RMSE) and mean bias were calculated. Position RMSE ranged from 1.68 to 16.39 m, while speed RMSE ranged from 0.34 to 2.38 m\,s^{-1}.…
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